Deep learning enabled fall detection exploiting gait analysis

Falls associated injuries often result not only increasing the medical-, social- and care-cost but also loss of mobility, impair chronic health and even potential risk of fatality. Because of elderly population growth, it is one of the major global public health problems. To address such issue, we present a Deep Learning enabled Fall Detection (DLFD) method exploiting Gait Analysis. More in details, firstly, we propose a framework for fall detection system. Secondly, we discussed the proposed DLFD method which exploits fall and non-fall RGB video to extract gait features using MediaPipe framework, applies normalization algorithm and classifies using bi-directional Long Short-Term Memory (bi-LSTM) model. Finally, the model is tested on collected three public datasets of 434mathrm{x}2 videos(more than 1 million frames) which consists of different activities and varieties of falls. The experimental results show that the model can achieve the accuracy of 96.35% and reveals the effectiveness of the proposal. This could play a significant role to alleviate falls problem by immediate alerting to emergency and relevant teams for taking necessary actions. This will speed up the assistance proceedings, reduce the risk of prolonged injury and save lives

Adaptive sequence detection and information capacity improvement for wireless communication systems

Increasing demand for wireless communication challenges the availability of limitedradio resources, such as bandwidth and power etc. Limited resources cause a tradeoff with the quality of service. The work presented in this thesis is intended to developalgorithms those can be used to demodulate information with optimal amountof resources (Signal to Noise Ratio, Processing memory requirement, Computationalcomplexity etc ).In the first part of the thesis a decision feedback sequence detection algorithm hasbeen proposed that provides exactly the same bit error rate as in standard maximumlikelihood sequence estimation but with 95% lowmomputational complexity. Besidesthat the proposed algorithm achieves 2 dB signal to noise ratio (SNR) gain overthe existing decision feedback algorithms. The proposed algorithm is applicable inmultiple input multiple output (MIMO) as well as single input single output (SISO)wireless communication systems.In the second part of the thesis an adaptive blind sequence detection algorithm hasbeen proposed where a novel reference channel has been exploited. The problem ofbit-shift ambiguity in blind sequence detection is completely eliminated exploitingthe proposed algorithm. A 3 dB SNR gain is achieved against the existing blindsequence detection algorithms for the system without error correction code. TheBER performance is highly scalable with the variation of segmentation window size.In the third part of the thesis, two different sequence detection algorithms havebeen proposed to track rapidly time varying channels. One of the algorithms, calledextended window survivor processing (EWSP), requires lower computational complexitythan that of Per-survivor processing (PSP) sequence detection process. Theother algorithm, called bi-directional survivor processing reduces 17% of channelmisacquisation than that of PSP. Consequently, both of these algorithms reduce theprobability of error propagation in the detection process.In the final part of the thesis, the capacity and coverage of the UMTS urban networkhas been analyzed while the Repeaters are inserted. It has been found that thesystem capacity with repeaters is doubled in an environment with the propagationconstant 3.7-3.8. As a by product, 10% increase in the cell coverage was also found